| Summary: | 博士 === 國立中央大學 === 地球科學學系 === 106 === Due to the lack of the physical mechanisms of rupture precursors, studies of earthquake precursors are still debated and skeptical. The purpose of this thesis is to verify correlations between pre-seismic anomalies of geoelectric fields and earthquakes. The thesis includes two
main components to examine and explain the geoelectric precursors to large earthquakes: the first is the field data analyses observed from Taiwan Geoelectric Monitoring System (GEMS), and the second is the analytical and numerical analyses of a seismo-electric model.
Beginning with field data analysis, we examine the precursory behavior of geoelectric data with respect to large earthquakes by means of an algorithm including a predictive model and binary classification. This algorithm is dubbed as GEMS’ Times of Increased Probability (GEMSTIP), introduced originally by Chen and Chen [Nat. Hazards., 84, 2016]. In the thesis, we improve the GEMSTIP model’s robustness (i) by removing a time parameter of coarsegraining in the foregoing paper, and (ii) by introducing joint stations method instead of single
station method. Moreover, the GEMSTIP algorithm includes Molchan Error Diagram (MED) to evaluate the performance of a model parameter set in a forecasting dataset. This improved GEMSTIP algorithm also analyzes a large number of high- and low-pass filtered datasets with different cutoff frequencies, determining the frequency bands, which were indefinite in previous works, of the earthquake-related signals with high signal-to-noise ratio for the geoelectric data. Based on significance tests derived from MED, the underlying pattern of seismo-electric
relationship is objectively thought to exist. It is therefore appropriate for machine learning to extract this underlying relationship to establish earthquake probability forecasts.
In the second part, according to the observed physics from indoor experiements of rock fracturing tests, we introduce the first fully self-consistent model combining the seismic micro-ruptures occurring within a generalized Burridge-Knopoff spring-block model with the nucleation and propagation of electric charge pulses within a coupled electrokinetic system (an RLC circuit model). This model, coined as Chen-Ouillon-Sornette (COS) model, provides a
general theoretical framework for modeling and analyzing the relationships between geoelectric signals and earthquakes. In particular, it is able to reproduce the unipolar pulses that have often been reported before large seismic events, as well as various observed anomalies of the ambient electric field, such as pre-seismic skewness and kurtosis anomalies [Chen and Chen, Nat. Hazards, 84, 2016; Chen et al., Terr. Atmos. Ocean. Sci., 28, 2017], and pre-seismic power-law exponent variations of power spectral densities of geoelectric fields [Eftaxias et al., Nat. Hazards Earth Syst. Sci., 3, 2003]. In consequence, this thesis strongly supports the theory of seismo-electric precursors, and lays the foundations for earthquake probability forecasts.
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